Many different types of physical phenomena may be modeled using numerical simulations. In the field of aerospace engineering, for example, numerical simulations are widely used to predict a variety of phenomena, including airflow over aerodynamic surfaces, electromagnetic scattering from reflective bodies, and mechanical stresses within structures. Examples of computational simulations also may be found in the fields of medical research, electrical engineering, geology, atmospheric sciences, and many other scientific fields. Such simulations may provide valuable information that may otherwise be very difficult and very expensive to determine experimentally. This is particularly true for models which include a large number of operations which would normally be performed in a parallel fashion in the real world but must be performed in serial fashion in the computer model due to a limited number of Central Processing Units (CPU's).
More specifically, in the field of radar, numerical simulations of radar recievers may be used to predict radar performance versus various targets. A common algorithm used in these simulations is the Fast Fourier Transform (FFT) which transforms a digitized waveform in the time domain into a digital representation in the frequency domain. FIG. 1 is a schematic view of a method 10 of performing simulations of the FFT in accordance with the prior art. As shown in FIG. 1, the method 10 includes receiving a first sine wave input 12 and a second swept frequency sine wave input 14. A mathematical converter 16 receives the first and second sine wave inputs 12, 14 via real and imaginary input ports 18, 20, and outputs a corresponding complex number output. An analyzer routine 22 performs a Fast Fourier Transform on the complex number output from the converter 16. Next, a mathematical de-converter 24 receives a FFT output from the analyzer routine 22 in complex form, and de-converts the FFT output into real and imaginary components, and outputs these components via real and imaginary output ports 26, 28, respectively, to a display device 30 (e.g. an oscilloscope) for further review and analysis. Using the simulation results displayed on the display device 30, the scientist or engineer may make further decisions regarding, for example, the frequency sweep of the radar transmitter, resolution of the doppler bins, or the design of the radar system that generates the incident electromagnetic signals. The method 10 is representative of at least some conventional methods for simulating radar signal processing using , one or more of the methods embodied in the SIMULINK simulation software developed by The Mathworks, Inc. of Natick, Mass.
Although desirable results have been achieved using the method 10, there is room for improvement. For example, some efforts to perform radar numerical simulation studies using the method 10 have been hampered by the intensity of the computations, resulting in lengthy computation times. In one case, for example, a numerical simulation of a radar receiver processor utilizing the method 10 required approximately two weeks of CPU time (336 CPU hours) on a modern high-speed computer to provide 1.6 seconds of real-time radar simulation data. Therefore, due to the ever-increasing requirements and demands being placed on numerical simulations there is a continuing impetus to improve the speed and efficiency, and to reduce the cost of such numerical simulations in both time and money.